by
Mike Thelwall, Kevan Buckley, Georgios Paltoglou
- Journal of the American Society for Information Science and Technology, 2012

"... Sentiment analysis is concerned with the automatic extraction of sentiment-related information from text. Although most sentiment analysis addresses commercial tasks, such as extracting opinions from product reviews, there is increasing interest in the affective dimension of the social web, and Twit ..."

Sentiment analysis is concerned with the automatic extraction of sentiment-related information from text. Although most sentiment analysis addresses commercial tasks, such as extracting opinions from product reviews, there is increasing interest in the affective dimension of the social web, and Twitter in particular. Most sentiment analysis algorithms are not ideally suited for this task because they exploit indirect indicators of sentiment that can reflect genre or topic instead. Hence, such algorithms used to process social web texts can identify spurious sentiment patterns caused by topics rather than affective phenomena. This article assesses an improved version of the algorithm SentiStrength for sentiment strength detection across the social web that primarily uses direct indications of sentiment. The results from six diverse social web data sets (MySpace, Twitter, YouTube, Digg, Runners World, BBC Forums) indicate that SentiStrength 2 is successful in the sense of performing better than a baseline approach for all data sets in both supervised and unsupervised cases. SentiStrength is not always better than machine learning approaches that exploit indirect indicators of sentiment, however, and is particularly weaker for positive sentiment in news-related discussions. Overall, the results suggest that, even unsupervised, SentiStrength is robust enough to be applied to a wide variety of different social web contexts.

...mpact on sentiment analysis for news (Balahur et al., 2010) or politics (Balahur, Kozareva, & Montoyo, 2009). Some commercial applications of sentiment analysis may also suffer from similar problems (=-=Taboada, Brooke, Tofiloski, Voll, & Stede, 2011-=-), as described below. From the above, it is sometimes critical to have classifiers that are only allowed to exploit direct indicators of sentiment. This is possible with a lexical approach: i.e., per...

by
Xia Hu, Jiliang Tang, Huiji Gao, Huan Liu
- In Proceedings of the 22nd international conference on World Wide Web, WWW’13. ACM

"... The explosion of social media services presents a great opportunity to understand the sentiment of the public via analyzing its large-scale and opinion-rich data. In social media, it is easy to amass vast quantities of unlabeled data, but very costly to obtain sentiment labels, which makes unsupervi ..."

The explosion of social media services presents a great opportunity to understand the sentiment of the public via analyzing its large-scale and opinion-rich data. In social media, it is easy to amass vast quantities of unlabeled data, but very costly to obtain sentiment labels, which makes unsupervised sentiment analysis essential for various applications. It is challenging for traditional lexicon-based unsupervised methods due to the fact that expressions in social media are unstructured, informal, and fast-evolving. Emoticons and product ratings are examples of emotional signals that are associated with sentiments expressed in posts or words. Inspired by the wide availability of emotional signals in social media, we propose to study the problem of unsupervised sentiment analysis with emotional signals. In particular, we investigate whether the signals can potentially help sentiment analysis by providing a unified way to model two main categories of emotional signals, i.e., emotion indication and emotion correlation. We further incorporate the signals into an unsupervised learning framework for sentiment analysis. In the experiment, we compare the proposed framework with the state-of-the-art methods on two Twitter datasets and empirically evaluate our proposed framework to gain a deep understanding of the effects of emotional signals.

"... We present a general learning-based approach for phrase-level sentiment analysis that adopts an ordinal sentiment scale and is explicitly compositional in nature. Thus, we can model the compositional effects required for accurate assignment of phrase-level sentiment. For example, combining an adverb ..."

We present a general learning-based approach for phrase-level sentiment analysis that adopts an ordinal sentiment scale and is explicitly compositional in nature. Thus, we can model the compositional effects required for accurate assignment of phrase-level sentiment. For example, combining an adverb (e.g., “very”) with a positive polar adjective (e.g., “good”) produces a phrase (“very good”) with increased polarity over the adjective alone. Inspired by recent work on distributional approaches to compositionality, we model each word as a matrix and combine words using iterated matrix multiplication, which allows for the modeling of both additive and multiplicative semantic effects. Although the multiplication-based matrix-space framework has been shown to be a theoretically elegant way to model composition (Rudolph and Giesbrecht, 2010), training such models has to be done carefully: the optimization is nonconvex and requires a good initial starting point. This paper presents the first such algorithm for learning a matrix-space model for semantic composition. In the context of the phrase-level sentiment analysis task, our experimental results show statistically significant improvements in performance over a bagof-words model. 1

... positive and negative labels for sentiment, negators are generally treated as flipping the polarity of the adjective it modifies (Choi and Cardie, 2008; Nakagawa et al., 2010). However, recent work (=-=Taboada et al., 2011-=-; Liu and Seneff, 2009) suggests that the effect of the negator when ordinal sentiment scores are employed is more akin to dampening the adjective’s polarity rather than flipping it. For example, if “...

"... Abstract We describe a state-of-the-art sentiment analysis system that detects (a) the sentiment of short informal textual messages such as tweets and SMS (message-level task) and (b) the sentiment of a word or a phrase within a message (term-level task). The system is based on a supervised statist ..."

Abstract We describe a state-of-the-art sentiment analysis system that detects (a) the sentiment of short informal textual messages such as tweets and SMS (message-level task) and (b) the sentiment of a word or a phrase within a message (term-level task). The system is based on a supervised statistical text classification approach leveraging a variety of surfaceform, semantic, and sentiment features. The sentiment features are primarily derived from novel high-coverage tweet-specific sentiment lexicons. These lexicons are automatically generated from tweets with sentiment-word hashtags and from tweets with emoticons. To adequately capture the sentiment of words in negated contexts, a separate sentiment lexicon is generated for negated words. The system ranked first in the SemEval-2013 shared task &apos;Sentiment Analysis in Twitter&apos; (Task 2), obtaining an F-score of 69.02 in the message-level task and 88.93 in the term-level task. Post-competition improvements boost the performance to an F-score of 70.45 (message-level task) and 89.50 (term-level task). The system also obtains state-ofthe-art performance on two additional datasets: the SemEval-2013 SMS test set and a corpus of movie review excerpts. The ablation experiments demonstrate that the use of the automatically generated lexicons results in performance gains of up to 6.5 absolute percentage points.

...is prior polarity can change. One such obvious contextual sentiment modifier is negation. In a negated context, many words change their polarity or at least the evaluative intensity. For example, the word good is often used to express positive attitude whereas the phrase not good is clearly negative. A conventional way of addressing negation in sentiment analysis is to reverse the polarity of a word, i.e. change a word’s sentiment score from s to −s (Kennedy & Inkpen, 2005; Choi & Cardie, 2008). However, several studies have pointed out the inadequacy of this solution (Kennedy & Inkpen, 2006; Taboada, Brooke, Tofiloski, Voll, & Stede, 2011). We will show through experiments in Section 4.3 that many positive terms, though not all, tend to reverse their polarity when negated, whereas most negative terms remain negative and only change their evaluative intensity. For example, the word terrible conveys a strong negative sentiment whereas the phrase wasn’t terrible is mildly negative. Also, the degree of the intensity shift varies from term to term for both positive and negative terms. To adequately capture the effects of negation on different terms, we propose a corpus-based statistical approach to estimate sentiment scores of indi...

"... With more than 10,000 new videos posted online every day on social websites such as YouTube and Facebook, the internet is becoming an almost infinite source of information. One crucial challenge for the coming decade is to be able to harvest relevant information from this constant flow of multimodal ..."

With more than 10,000 new videos posted online every day on social websites such as YouTube and Facebook, the internet is becoming an almost infinite source of information. One crucial challenge for the coming decade is to be able to harvest relevant information from this constant flow of multimodal data. This paper addresses the task of multimodal sentiment analysis, and conducts proof-of-concept experiments that demonstrate that a joint model that integrates visual, audio, and textual features can be effectively used to identify sentiment in Web videos. This paper makes three important contributions. First, it addresses for the first time the task of tri-modal sentiment analysis, and shows that it is a feasible task that can benefit from the joint exploitation of visual, audio and textual modalities. Second, it identifies a subset of audio-visual features relevant to sentiment analysis and present guidelines on how to integrate these features. Finally, it introduces a new dataset consisting of real online data, which will be useful for future research in this area.

...le and the bottom blue line represents the 25th percentile. From these results, many interesting observations can be made: • Polarized words: As shown in previous work on text-based sentiment analysis=-=[15, 26]-=-, using a dictionary of positively or negatively polarized words, sentiment polarity can be effectively differentiated. One of the main issues with using only textual features is that most utterances ...

"... We propose a joint model for unsupervised induction of sentiment, aspect and discourse information and show that by incorporating a notion of latent discourse relations in the model, we improve the prediction accuracy for aspect and sentiment polarity on the sub-sentential level. We deviate from the ..."

We propose a joint model for unsupervised induction of sentiment, aspect and discourse information and show that by incorporating a notion of latent discourse relations in the model, we improve the prediction accuracy for aspect and sentiment polarity on the sub-sentential level. We deviate from the traditional view of discourse, as we induce types of discourse relations and associated discourse cues relevant to the considered opinion analysis task; consequently, the induced discourse relations play the role of opinion and aspect shifters. The quantitative analysis that we conducted indicated that the integration of a discourse model increased the prediction accuracy results with respect to the discourse-agnostic approach and the qualitative analysis suggests that the induced representations encode a meaningful discourse structure. 1

...course has been used in order to enforce constraints on the assignment of polarity labels at several granularity levels, ranging from the lexical level (Polanyi and Zaenen, 2006) to the review level (=-=Taboada et al., 2011-=-). One way to deal with this problem is to model the interactions by using a precompiled set of polarity shifters (Nakagawa et al., 2010; Polanyi and Zaenen, 2006; Sadamitsu et al., 2008). Socher et a...

"... Abstract Sarcasm is a common phenomenon in social media, and is inherently difficult to analyse, not just automatically but often for humans too. It has an important effect on sentiment, but is usually ignored in social media analysis, because it is considered too tricky to handle. While there exis ..."

Abstract Sarcasm is a common phenomenon in social media, and is inherently difficult to analyse, not just automatically but often for humans too. It has an important effect on sentiment, but is usually ignored in social media analysis, because it is considered too tricky to handle. While there exist a few systems which can detect sarcasm, almost no work has been carried out on studying the effect that sarcasm has on sentiment in tweets, and on incorporating this into automatic tools for sentiment analysis. We perform an analysis of the effect of sarcasm scope on the polarity of tweets, and have compiled a number of rules which enable us to improve the accuracy of sentiment analysis when sarcasm is known to be present. We consider in particular the effect of sentiment and sarcasm contained in hashtags, and have developed a hashtag tokeniser for GATE, so that sentiment and sarcasm found within hashtags can be detected more easily. According to our experiments, the hashtag tokenisation achieves 98% Precision, while the sarcasm detection achieved 91% Precision and polarity detection 80%.

...r names in 5http://semanticnews.org.uk Twitter by means of DBpedia; we are planning to experiment with adapting this technique to hashtags also, so that such entities can be recognised. We could also investigate using a language modelling approach based on unigram or bigram frequencies, such as used by Berardi et al. (Berardi et al., 2011). 5. Sentiment analysis of tweets For the experiments in this paper, we have used the GATEbased sentiment analysis system developed as part of the Arcomem project (Maynard et al., 2012). This adopts a lexicon-based approach similar to that of Taboada et al. (Taboada et al., 2011), incorporating a series of intensifiers and negators which alter the polarity score. To investigate the effect of sarcasm on tweets, we collected a corpus of 134 tweets containing the hashtag #sarcasm, from a larger set acquired via GardenHose on Oct 16 2012 (Preotiuc-Pietro et al., 2012), and manually annotated the sentences with a sentiment (positive, negative or no sentiment). Out of 266 sentences, 68 were found to be opinionated (approximately 25%), and of these 62 were negative while 6 were positive. Of these 68 opinionated sentences, 61 were deemed to be influenced by sarcasm while 8 we...